Clustered Subsampling of Double Sampling for Stratification and Growth Model Based Updates of Past Forest Inventories

分层双重抽样是一种抽样设计，广泛用于世界范围内的森林和资源清查，特别是在德国公共和私人森林地区的定期森林清查中得到了很好的应用。第二阶段单位的空间聚集子抽样实际上代表第三阶段的抽样，可以预期减少旅行费用，但也会降低估计的精度。因此，拟议项目的目的是在新的三期抽样设计下，对森林清单中通常目标变量的总数和每公顷价值以及有关的抽样误差进行估计。使用实际数据，分析精度和聚类数量之间的权衡。一个特别的重点将是在以前的双重抽样地区清单的基础上建立临时的区域或全州清单。在这种情况下，可以通过使用增长模型更新以前的库存来获得额外的精度。这些增长预测应与基于样本的估计量相结合，形成更高精度的复合估计量。

Double Sampling for Stratification is a sampling design that is widely used for forest and resource inventories worldwide and, particularly, well established for periodic forest inventories of districts in public and private forests in Germany. Spatially clustered subsampling of second-phase units, actually representing a third phase of sampling, can be expected to reduce travelling costs, but will also decrease precision of estimates. Therefore, the proposed project is intended to develop estimators for totals and per hectare values of usual target variables in forest inventories as well as related sampling errors under that new three-phase sampling design. Using real data the trade-off between precision and amount of clustering will be analyzed. A special focus will be on temporary regional or state-wide inventories based on previous double sampling district inventories. In this case additional precision can be gained by updating the previous inventories using growth models. These growth predictions shall be combined with the sample based estimator to form a composite estimator of higher precision.